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Choe J, Hwang HJ, Lee SM, Yoon J, Kim N, Seo JB. CT Quantification of Interstitial Lung Abnormality and Interstitial Lung Disease: From Technical Challenges to Future Directions. Invest Radiol 2024:00004424-990000000-00233. [PMID: 39008898 DOI: 10.1097/rli.0000000000001103] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/17/2024]
Abstract
ABSTRACT Interstitial lung disease (ILD) encompasses a variety of lung disorders with varying degrees of inflammation or fibrosis, requiring a combination of clinical, imaging, and pathologic data for evaluation. Imaging is essential for the noninvasive diagnosis of the disease, as well as for assessing disease severity, monitoring its progression, and evaluating treatment response. However, traditional visual assessments of ILD with computed tomography (CT) suffer from reader variability. Automated quantitative CT offers a more objective approach by using computer-based analysis to consistently evaluate and measure ILD. Advancements in technology have significantly improved the accuracy and reliability of these measurements. Recently, interstitial lung abnormalities (ILAs), which represent potential preclinical ILD incidentally found on CT scans and are characterized by abnormalities in over 5% of any lung zone, have gained attention and clinical importance. The challenge lies in the accurate and consistent identification of ILA, given that its definition relies on a subjective threshold, making quantitative tools crucial for precise ILA evaluation. This review highlights the state of CT quantification of ILD and ILA, addressing clinical and research disparities while emphasizing how machine learning or deep learning in quantitative imaging can improve diagnosis and management by providing more accurate assessments, and finally, suggests the future directions of quantitative CT in this area.
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Affiliation(s)
- Jooae Choe
- From the Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, South Korea (J.C., H.J.H., S.M.L., J.Y., N.K., J.B.S.); and Department of Convergence Medicine, Biomedical Engineering Research Center, University of Ulsan College of Medicine, Asan Medical Center, Seoul, South Korea (J.Y. and N.K.)
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Wu J, Lu Y, Dong S, Wu L, Shen X. Predicting COPD exacerbations based on quantitative CT analysis: an external validation study. Front Med (Lausanne) 2024; 11:1370917. [PMID: 38933101 PMCID: PMC11199769 DOI: 10.3389/fmed.2024.1370917] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2024] [Accepted: 05/30/2024] [Indexed: 06/28/2024] Open
Abstract
Purpose Quantitative computed tomography (CT) analysis is an important method for diagnosis and severity evaluation of lung diseases. However, the association between CT-derived biomarkers and chronic obstructive pulmonary disease (COPD) exacerbations remains unclear. We aimed to investigate its potential in predicting COPD exacerbations. Methods Patients with COPD were consecutively enrolled, and their data were analyzed in this retrospective study. Body composition and thoracic abnormalities were analyzed from chest CT scans. Logistic regression analysis was performed to identify independent risk factors of exacerbation. Based on 2-year follow-up data, the deep learning system (DLS) was developed to predict future exacerbations. Receiver operating characteristic (ROC) curve analysis was conducted to assess the diagnostic performance. Finally, the survival analysis was performed to further evaluate the potential of the DLS in risk stratification. Results A total of 1,150 eligible patients were included and followed up for 2 years. Multivariate analysis revealed that CT-derived high affected lung volume/total lung capacity (ALV/TLC) ratio, high visceral adipose tissue area (VAT), and low pectoralis muscle cross-sectional area (CSA) were independent risk factors causing COPD exacerbations. The DLS outperformed exacerbation history and the BMI, airflow obstruction, dyspnea, and exercise capacity (BODE) index, with an area under the ROC (AUC) value of 0.88 (95%CI, 0.82-0.92) in the internal cohort and 0.86 (95%CI, 0.81-0.89) in the external cohort. The DeLong test revealed significance between this system and conventional scores in the test cohorts (p < 0.05). In the survival analysis, patients with higher risk were susceptible to exacerbation events. Conclusion The DLS could allow accurate prediction of COPD exacerbations. The newly identified CT biomarkers (ALV/TLC ratio, VAT, and pectoralis muscle CSA) could potentially enable investigation into underlying mechanisms responsible for exacerbations.
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Affiliation(s)
- Ji Wu
- Department of General Surgery, Suzhou Ninth Hospital Affiliated to Soochow University, Suzhou, China
| | - Yao Lu
- Department of Anesthesia, Fifth People's Hospital of Wujiang District, Suzhou, China
| | - Sunbin Dong
- Department of General Medicine, Municipal Hospital, Suzhou, China
| | - Luyang Wu
- Department of General Medicine, Municipal Hospital, Suzhou, China
| | - Xiping Shen
- Department of General Surgery, Suzhou Ninth Hospital Affiliated to Soochow University, Suzhou, China
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Humphries SM, Thieke D, Baraghoshi D, Strand MJ, Swigris JJ, Chae KJ, Hwang HJ, Oh AS, Flaherty KR, Adegunsoye A, Jablonski R, Lee CT, Husain AN, Chung JH, Strek ME, Lynch DA. Deep Learning Classification of Usual Interstitial Pneumonia Predicts Outcomes. Am J Respir Crit Care Med 2024; 209:1121-1131. [PMID: 38207093 DOI: 10.1164/rccm.202307-1191oc] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2023] [Accepted: 01/04/2024] [Indexed: 01/13/2024] Open
Abstract
Rationale: Computed tomography (CT) enables noninvasive diagnosis of usual interstitial pneumonia (UIP), but enhanced image analyses are needed to overcome the limitations of visual assessment. Objectives: Apply multiple instance learning (MIL) to develop an explainable deep learning algorithm for prediction of UIP from CT and validate its performance in independent cohorts. Methods: We trained an MIL algorithm using a pooled dataset (n = 2,143) and tested it in three independent populations: data from a prior publication (n = 127), a single-institution clinical cohort (n = 239), and a national registry of patients with pulmonary fibrosis (n = 979). We tested UIP classification performance using receiver operating characteristic analysis, with histologic UIP as ground truth. Cox proportional hazards and linear mixed-effects models were used to examine associations between MIL predictions and survival or longitudinal FVC. Measurements and Main Results: In two cohorts with biopsy data, MIL improved accuracy for histologic UIP (area under the curve, 0.77 [n = 127] and 0.79 [n = 239]) compared with visual assessment (area under the curve, 0.65 and 0.71). In cohorts with survival data, MIL-UIP classifications were significant for mortality (n = 239, mortality to April 2021: unadjusted hazard ratio, 3.1; 95% confidence interval [CI], 1.96-4.91; P < 0.001; and n = 979, mortality to July 2022: unadjusted hazard ratio, 3.64; 95% CI, 2.66-4.97; P < 0.001). Individuals classified as UIP positive by the algorithm had a significantly greater annual decline in FVC than those classified as UIP negative (-88 ml/yr vs. -45 ml/yr; n = 979; P < 0.01), adjusting for extent of lung fibrosis. Conclusions: Computerized assessment using MIL identifies clinically significant features of UIP on CT. Such a method could improve confidence in radiologic assessment of patients with interstitial lung disease, potentially enabling earlier and more precise diagnosis.
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Affiliation(s)
| | | | | | | | - Jeffrey J Swigris
- Division of Pulmonary and Critical Care Medicine, National Jewish Health, Denver, Colorado
| | - Kum Ju Chae
- Department of Radiology, Research Institute of Clinical Medicine of Jeonbuk National University-Biomedical Research Institute of Jeonbuk National University Hospital, Jeonju, South Korea
| | - Hye Jeon Hwang
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, South Korea
| | - Andrea S Oh
- Department of Radiology, University of California Los Angeles, Los Angeles, California
| | - Kevin R Flaherty
- Division of Pulmonary and Critical Care Medicine, University of Michigan, Ann Arbor, Michigan
| | | | - Renea Jablonski
- Section of Pulmonary and Critical Care, Department of Medicine
| | - Cathryn T Lee
- Section of Pulmonary and Critical Care, Department of Medicine
| | - Aliya N Husain
- Department of Pathology, The University of Chicago, Chicago, Illinois
| | | | - Mary E Strek
- Section of Pulmonary and Critical Care, Department of Medicine
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Kanne JP, Walker CM, Brixey AG, Brown KK, Chelala L, Kazerooni EA, Walsh SLF, Lynch DA. Progressive Pulmonary Fibrosis and Interstitial Lung Abnormalities: AJR Expert Panel Narrative Review. AJR Am J Roentgenol 2024. [PMID: 38656115 DOI: 10.2214/ajr.24.31125] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/26/2024]
Abstract
Progressive pulmonary fibrosis (PPF) and interstitial lung abnormalities (ILA) are relatively new concepts in interstitial lung disease (ILD) imaging and clinical management. Recognition of signs of PPF, as well as identification and classification of ILA, are important tasks during chest high-resolution CT interpretation, to optimize management of patients with ILD and those at risk of developing ILD. However, following professional society guidance, the role of imaging surveillance remains unclear in stable patients with ILD, asymptomatic patients with ILA who are at risk of progression, and asymptomatic patients at risk of developing ILD without imaging abnormalities. In this AJR Expert Panel Narrative Review, we summarize the current knowledge regarding PPF and ILA and describe the range of clinical practice with respect to imaging patients with ILD, those with ILA, and those at risk of developing ILD. In addition, we offer suggestions to help guide surveillance imaging in areas with an absence of published guidelines, where such decisions are currently driven primarily by local pulmonologists' preference.
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Affiliation(s)
- Jeffrey P Kanne
- Department of Radiology, University of Wisconsin School of Medicine and Public Health, Madison, WI
| | - Christopher M Walker
- Department of Radiology, The University of Kansas Medical Center, Kansas City, KS
| | - Anupama G Brixey
- Department of Radiology, Portland VA Healthcare System, Oregon Health & Science University, Portland, OR
| | - Kevin K Brown
- Department of Medicine, National Jewish Health, Denver, CO
| | - Lydia Chelala
- Department of Radiology, University of Chicago Medicine, Chicago, IL
| | - Ella A Kazerooni
- Departments of Radiology & Internal Medicine, University of Michigan Medical School / Michigan Medicine, Ann Arbor, MI
| | - Simon L F Walsh
- Department of Radiology, Imperial College, London, United Kingdom
| | - David A Lynch
- Department of Radiology, National Jewish Health, Denver, CO
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Shiraishi Y, Tanabe N, Sakamoto R, Maetani T, Kaji S, Shima H, Terada S, Terada K, Ikezoe K, Tanizawa K, Oguma T, Handa T, Sato S, Muro S, Hirai T. Longitudinal assessment of interstitial lung abnormalities on CT in patients with COPD using artificial intelligence-based segmentation: a prospective observational study. BMC Pulm Med 2024; 24:200. [PMID: 38654252 PMCID: PMC11036664 DOI: 10.1186/s12890-024-03002-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2023] [Accepted: 04/09/2024] [Indexed: 04/25/2024] Open
Abstract
BACKGROUND Interstitial lung abnormalities (ILAs) on CT may affect the clinical outcomes in patients with chronic obstructive pulmonary disease (COPD), but their quantification remains unestablished. This study examined whether artificial intelligence (AI)-based segmentation could be applied to identify ILAs using two COPD cohorts. METHODS ILAs were diagnosed visually based on the Fleischner Society definition. Using an AI-based method, ground-glass opacities, reticulations, and honeycombing were segmented, and their volumes were summed to obtain the percentage ratio of interstitial lung disease-associated volume to total lung volume (ILDvol%). The optimal ILDvol% threshold for ILA detection was determined in cross-sectional data of the discovery and validation cohorts. The 5-year longitudinal changes in ILDvol% were calculated in discovery cohort patients who underwent baseline and follow-up CT scans. RESULTS ILAs were found in 32 (14%) and 15 (10%) patients with COPD in the discovery (n = 234) and validation (n = 153) cohorts, respectively. ILDvol% was higher in patients with ILAs than in those without ILA in both cohorts. The optimal ILDvol% threshold in the discovery cohort was 1.203%, and good sensitivity and specificity (93.3% and 76.3%) were confirmed in the validation cohort. 124 patients took follow-up CT scan during 5 ± 1 years. 8 out of 124 patients (7%) developed ILAs. In a multivariable model, an increase in ILDvol% was associated with ILA development after adjusting for age, sex, BMI, and smoking exposure. CONCLUSION AI-based CT quantification of ILDvol% may be a reproducible method for identifying and monitoring ILAs in patients with COPD.
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Affiliation(s)
- Yusuke Shiraishi
- Department of Respiratory Medicine, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Naoya Tanabe
- Department of Respiratory Medicine, Graduate School of Medicine, Kyoto University, Kyoto, Japan.
- Department of Respiratory Medicine, Graduate School of Medicine, Kyoto University, 54 Kawahara-cho, Shogoin, Sakyo-ku, 606-8507, Kyoto, Kyoto, Japan.
| | - Ryo Sakamoto
- Department of Diagnostic Imaging and Nuclear Medicine, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Tomoki Maetani
- Department of Respiratory Medicine, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Shizuo Kaji
- Institute of Mathematics for Industry, Kyusyu University, Fukuoka, Japan
| | - Hiroshi Shima
- Department of Respiratory Medicine, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Satoru Terada
- Department of Respiratory Medicine, Graduate School of Medicine, Kyoto University, Kyoto, Japan
- Respiratory Medicine and General Practice, Terada Clinic, Himeji, Hyogo, Japan
| | - Kunihiko Terada
- Respiratory Medicine and General Practice, Terada Clinic, Himeji, Hyogo, Japan
| | - Kohei Ikezoe
- Department of Respiratory Medicine, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Kiminobu Tanizawa
- Department of Respiratory Medicine, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Tsuyoshi Oguma
- Department of Respiratory Medicine, Graduate School of Medicine, Kyoto University, Kyoto, Japan
- Department of Respiratory Medicine, Kyoto City Hospital, Kyoto, Japan
| | - Tomohiro Handa
- Department of Respiratory Medicine, Graduate School of Medicine, Kyoto University, Kyoto, Japan
- Department of Advanced Medicine for Respiratory Failure, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Susumu Sato
- Department of Respiratory Medicine, Graduate School of Medicine, Kyoto University, Kyoto, Japan
- Department of Respiratory Care and Sleep Control Medicine, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Shigeo Muro
- Department of Respiratory Medicine, Nara Medical University, Kashihara, Nara, Japan
| | - Toyohiro Hirai
- Department of Respiratory Medicine, Graduate School of Medicine, Kyoto University, Kyoto, Japan
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Choi B, Díaz AA, San José Estépar R, Enzer N, Castro V, Han MK, Washko GR, San José Estépar R, Ash SY. Association of Acute Respiratory Disease Events with Quantitative Interstitial Abnormality Progression at CT in Individuals with a History of Smoking. Radiology 2024; 311:e231801. [PMID: 38687222 PMCID: PMC11070608 DOI: 10.1148/radiol.231801] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2023] [Revised: 02/24/2024] [Accepted: 03/06/2024] [Indexed: 05/02/2024]
Abstract
Background Acute respiratory disease (ARD) events are often thought to be airway-disease related, but some may be related to quantitative interstitial abnormalities (QIAs), which are subtle parenchymal abnormalities on CT scans associated with morbidity and mortality in individuals with a smoking history. Purpose To determine whether QIA progression at CT is associated with ARD and severe ARD events in individuals with a history of smoking. Materials and Methods This secondary analysis of a prospective study included individuals with a 10 pack-years or greater smoking history recruited from multiple centers between November 2007 and July 2017. QIA progression was assessed between baseline (visit 1) and 5-year follow-up (visit 2) chest CT scans. Episodes of ARD were defined as increased cough or dyspnea lasting 48 hours and requiring antibiotics or corticosteroids, whereas severe ARD episodes were those requiring an emergency room visit or hospitalization. Episodes were recorded via questionnaires completed every 3 to 6 months. Multivariable logistic regression and zero-inflated negative binomial regression models adjusted for comorbidities (eg, emphysema, small airway disease) were used to assess the association between QIA progression and episodes between visits 1 and 2 (intercurrent) and after visit 2 (subsequent). Results A total of 3972 participants (mean age at baseline, 60.7 years ± 8.6 [SD]; 2120 [53.4%] women) were included. Annual percentage QIA progression was associated with increased odds of one or more intercurrent (odds ratio [OR] = 1.29 [95% CI: 1.06, 1.56]; P = .01) and subsequent (OR = 1.26 [95% CI: 1.05, 1.52]; P = .02) severe ARD events. Participants in the highest quartile of QIA progression (≥1.2%) had more frequent intercurrent ARD (incidence rate ratio [IRR] = 1.46 [95% CI: 1.14, 1.86]; P = .003) and severe ARD (IRR = 1.79 [95% CI: 1.18, 2.73]; P = .006) events than those in the lowest quartile (≤-1.7%). Conclusion QIA progression was independently associated with higher odds of severe ARD events during and after radiographic progression, with higher frequency of intercurrent severe events in those with faster progression. Clinical trial registration no. NCT00608764 © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Little in this issue.
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Affiliation(s)
- Bina Choi
- From the Division of Pulmonary and Critical Care Medicine, Department
of Medicine (B.C., A.A.D., G.R.W.), Applied Chest Imaging Laboratory (B.C.,
A.A.D., Ruben San José Estépar, N.E., G.R.W., Raúl San
José Estépar), and Department of Radiology (Ruben San José
Estépar, Raúl San José Estépar), Brigham and
Women’s Hospital, 15 Francis St, Boston, MA 02115; Boston University
School of Medicine, Boston, Mass (V.C.); Division of Pulmonary and Critical Care
Medicine, Department of Internal Medicine, University of Michigan, Ann Arbor,
Mich (M.K.H.); Department of Critical Care Medicine, South Shore Health, South
Weymouth, Mass (S.Y.A.); and Tufts University School of Medicine, Boston, Mass
(S.Y.A.)
| | - Alejandro A. Díaz
- From the Division of Pulmonary and Critical Care Medicine, Department
of Medicine (B.C., A.A.D., G.R.W.), Applied Chest Imaging Laboratory (B.C.,
A.A.D., Ruben San José Estépar, N.E., G.R.W., Raúl San
José Estépar), and Department of Radiology (Ruben San José
Estépar, Raúl San José Estépar), Brigham and
Women’s Hospital, 15 Francis St, Boston, MA 02115; Boston University
School of Medicine, Boston, Mass (V.C.); Division of Pulmonary and Critical Care
Medicine, Department of Internal Medicine, University of Michigan, Ann Arbor,
Mich (M.K.H.); Department of Critical Care Medicine, South Shore Health, South
Weymouth, Mass (S.Y.A.); and Tufts University School of Medicine, Boston, Mass
(S.Y.A.)
| | - Ruben San José Estépar
- From the Division of Pulmonary and Critical Care Medicine, Department
of Medicine (B.C., A.A.D., G.R.W.), Applied Chest Imaging Laboratory (B.C.,
A.A.D., Ruben San José Estépar, N.E., G.R.W., Raúl San
José Estépar), and Department of Radiology (Ruben San José
Estépar, Raúl San José Estépar), Brigham and
Women’s Hospital, 15 Francis St, Boston, MA 02115; Boston University
School of Medicine, Boston, Mass (V.C.); Division of Pulmonary and Critical Care
Medicine, Department of Internal Medicine, University of Michigan, Ann Arbor,
Mich (M.K.H.); Department of Critical Care Medicine, South Shore Health, South
Weymouth, Mass (S.Y.A.); and Tufts University School of Medicine, Boston, Mass
(S.Y.A.)
| | - Nicholas Enzer
- From the Division of Pulmonary and Critical Care Medicine, Department
of Medicine (B.C., A.A.D., G.R.W.), Applied Chest Imaging Laboratory (B.C.,
A.A.D., Ruben San José Estépar, N.E., G.R.W., Raúl San
José Estépar), and Department of Radiology (Ruben San José
Estépar, Raúl San José Estépar), Brigham and
Women’s Hospital, 15 Francis St, Boston, MA 02115; Boston University
School of Medicine, Boston, Mass (V.C.); Division of Pulmonary and Critical Care
Medicine, Department of Internal Medicine, University of Michigan, Ann Arbor,
Mich (M.K.H.); Department of Critical Care Medicine, South Shore Health, South
Weymouth, Mass (S.Y.A.); and Tufts University School of Medicine, Boston, Mass
(S.Y.A.)
| | - Victor Castro
- From the Division of Pulmonary and Critical Care Medicine, Department
of Medicine (B.C., A.A.D., G.R.W.), Applied Chest Imaging Laboratory (B.C.,
A.A.D., Ruben San José Estépar, N.E., G.R.W., Raúl San
José Estépar), and Department of Radiology (Ruben San José
Estépar, Raúl San José Estépar), Brigham and
Women’s Hospital, 15 Francis St, Boston, MA 02115; Boston University
School of Medicine, Boston, Mass (V.C.); Division of Pulmonary and Critical Care
Medicine, Department of Internal Medicine, University of Michigan, Ann Arbor,
Mich (M.K.H.); Department of Critical Care Medicine, South Shore Health, South
Weymouth, Mass (S.Y.A.); and Tufts University School of Medicine, Boston, Mass
(S.Y.A.)
| | - MeiLan K. Han
- From the Division of Pulmonary and Critical Care Medicine, Department
of Medicine (B.C., A.A.D., G.R.W.), Applied Chest Imaging Laboratory (B.C.,
A.A.D., Ruben San José Estépar, N.E., G.R.W., Raúl San
José Estépar), and Department of Radiology (Ruben San José
Estépar, Raúl San José Estépar), Brigham and
Women’s Hospital, 15 Francis St, Boston, MA 02115; Boston University
School of Medicine, Boston, Mass (V.C.); Division of Pulmonary and Critical Care
Medicine, Department of Internal Medicine, University of Michigan, Ann Arbor,
Mich (M.K.H.); Department of Critical Care Medicine, South Shore Health, South
Weymouth, Mass (S.Y.A.); and Tufts University School of Medicine, Boston, Mass
(S.Y.A.)
| | - George R. Washko
- From the Division of Pulmonary and Critical Care Medicine, Department
of Medicine (B.C., A.A.D., G.R.W.), Applied Chest Imaging Laboratory (B.C.,
A.A.D., Ruben San José Estépar, N.E., G.R.W., Raúl San
José Estépar), and Department of Radiology (Ruben San José
Estépar, Raúl San José Estépar), Brigham and
Women’s Hospital, 15 Francis St, Boston, MA 02115; Boston University
School of Medicine, Boston, Mass (V.C.); Division of Pulmonary and Critical Care
Medicine, Department of Internal Medicine, University of Michigan, Ann Arbor,
Mich (M.K.H.); Department of Critical Care Medicine, South Shore Health, South
Weymouth, Mass (S.Y.A.); and Tufts University School of Medicine, Boston, Mass
(S.Y.A.)
| | - Raúl San José Estépar
- From the Division of Pulmonary and Critical Care Medicine, Department
of Medicine (B.C., A.A.D., G.R.W.), Applied Chest Imaging Laboratory (B.C.,
A.A.D., Ruben San José Estépar, N.E., G.R.W., Raúl San
José Estépar), and Department of Radiology (Ruben San José
Estépar, Raúl San José Estépar), Brigham and
Women’s Hospital, 15 Francis St, Boston, MA 02115; Boston University
School of Medicine, Boston, Mass (V.C.); Division of Pulmonary and Critical Care
Medicine, Department of Internal Medicine, University of Michigan, Ann Arbor,
Mich (M.K.H.); Department of Critical Care Medicine, South Shore Health, South
Weymouth, Mass (S.Y.A.); and Tufts University School of Medicine, Boston, Mass
(S.Y.A.)
| | - Samuel Y. Ash
- From the Division of Pulmonary and Critical Care Medicine, Department
of Medicine (B.C., A.A.D., G.R.W.), Applied Chest Imaging Laboratory (B.C.,
A.A.D., Ruben San José Estépar, N.E., G.R.W., Raúl San
José Estépar), and Department of Radiology (Ruben San José
Estépar, Raúl San José Estépar), Brigham and
Women’s Hospital, 15 Francis St, Boston, MA 02115; Boston University
School of Medicine, Boston, Mass (V.C.); Division of Pulmonary and Critical Care
Medicine, Department of Internal Medicine, University of Michigan, Ann Arbor,
Mich (M.K.H.); Department of Critical Care Medicine, South Shore Health, South
Weymouth, Mass (S.Y.A.); and Tufts University School of Medicine, Boston, Mass
(S.Y.A.)
| | - for the COPDGene Study
- From the Division of Pulmonary and Critical Care Medicine, Department
of Medicine (B.C., A.A.D., G.R.W.), Applied Chest Imaging Laboratory (B.C.,
A.A.D., Ruben San José Estépar, N.E., G.R.W., Raúl San
José Estépar), and Department of Radiology (Ruben San José
Estépar, Raúl San José Estépar), Brigham and
Women’s Hospital, 15 Francis St, Boston, MA 02115; Boston University
School of Medicine, Boston, Mass (V.C.); Division of Pulmonary and Critical Care
Medicine, Department of Internal Medicine, University of Michigan, Ann Arbor,
Mich (M.K.H.); Department of Critical Care Medicine, South Shore Health, South
Weymouth, Mass (S.Y.A.); and Tufts University School of Medicine, Boston, Mass
(S.Y.A.)
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7
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Ash S, Doyle TJ, Choi B, San Jose Estepar R, Castro V, Enzer N, Kalhan R, Liu G, Bowler R, Wilson DO, San Jose Estepar R, Rosas IO, Washko GR. Utility of peripheral protein biomarkers for the prediction of incident interstitial features: a multicentre retrospective cohort study. BMJ Open Respir Res 2024; 11:e002219. [PMID: 38485250 PMCID: PMC10941119 DOI: 10.1136/bmjresp-2023-002219] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2023] [Accepted: 02/28/2024] [Indexed: 03/17/2024] Open
Abstract
INTRODUCTION/RATIONALE Protein biomarkers may help enable the prediction of incident interstitial features on chest CT. METHODS We identified which protein biomarkers in a cohort of smokers (COPDGene) differed between those with and without objectively measured interstitial features at baseline using a univariate screen (t-test false discovery rate, FDR p<0.001), and which of those were associated with interstitial features longitudinally (multivariable mixed effects model FDR p<0.05). To predict incident interstitial features, we trained four random forest classifiers in a two-thirds random subset of COPDGene: (1) imaging and demographic information, (2) univariate screen biomarkers, (3) multivariable confirmation biomarkers and (4) multivariable confirmation biomarkers available in a separate testing cohort (Pittsburgh Lung Screening Study (PLuSS)). We evaluated classifier performance in the remaining one-third of COPDGene, and, for the final model, also in PLuSS. RESULTS In COPDGene, 1305 biomarkers were available and 20 differed between those with and without interstitial features at baseline. Of these, 11 were associated with feature progression over a mean of 5.5 years of follow-up, and of these 4 were available in PLuSS, (angiopoietin-2, matrix metalloproteinase 7, macrophage inflammatory protein 1 alpha) over a mean of 8.8 years of follow-up. The area under the curve (AUC) of classifiers using demographics and imaging features in COPDGene and PLuSS were 0.69 and 0.59, respectively. In COPDGene, the AUC of the univariate screen classifier was 0.78 and of the multivariable confirmation classifier was 0.76. The AUC of the final classifier in COPDGene was 0.75 and in PLuSS was 0.76. The outcome for all of the models was the development of incident interstitial features. CONCLUSIONS Multiple novel and previously identified proteomic biomarkers are associated with interstitial features on chest CT and may enable the prediction of incident interstitial diseases such as idiopathic pulmonary fibrosis.
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Affiliation(s)
- Samuel Ash
- Department of Critical Care Medicine, South Shore Hospital, South Weymouth, Massachusetts, USA
- Tufts University School of Medicine, Boston, Massachusetts, USA
| | - Tracy J Doyle
- Pulmonary and Critical Care Division, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Bina Choi
- Department of Medicine, Brigham and Women's Hospital, Boston, Massachusetts, USA
| | | | - Victor Castro
- Boston University School of Medicine, Boston, Massachusetts, USA
| | - Nicholas Enzer
- Department of Medicine, Brigham and Women's Hospital, Boston, Massachusetts, USA
| | - Ravi Kalhan
- Division of Pulmonary/Critical Care, Northwestern University, Chicago, Illinois, USA
| | - Gabrielle Liu
- Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA
| | | | - David O Wilson
- Medicine, Pulmonary Division, University of Pittsburgh, pittsburgh, Pennsylvania, USA
| | - Raul San Jose Estepar
- Applied Chest Imaging Laboratory, Brigham and Women's Hospital, Boston, Massachusetts, USA
| | - Ivan O Rosas
- Department of Medicine: Pulmonary, Critical Care and Sleep Medicine, Baylor College of Medicine, Houston, Texas, USA
| | - George R Washko
- Pulmonary and Critical Care Medicine, Brigham and Women's Hospital/Harvard Medical School, Boston, Massachusetts, USA
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8
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McDermott GC, Hayashi K, Yoshida K, Juge PA, Moll M, Cho MH, Doyle TJ, Kinney GL, Dellaripa PF, Wallace ZS, Regan EA, Hunninghake GM, Silverman EK, Ash SY, Estepar RSJ, Washko GR, Sparks JA. Rheumatoid arthritis, quantitative parenchymal lung features, and mortality among smokers. Rheumatology (Oxford) 2023:kead645. [PMID: 38048611 DOI: 10.1093/rheumatology/kead645] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2023] [Revised: 10/31/2023] [Accepted: 11/05/2023] [Indexed: 12/06/2023] Open
Abstract
OBJECTIVES There have been limited investigations of the prevalence and mortality impact of quantitative computed tomography (QCT) parenchymal lung features in rheumatoid arthritis (RA). We examined the cross-sectional prevalence and mortality associations of QCT features, comparing RA and non-RA participants. METHODS We identified participants with and without RA in COPDGene, a multicentre cohort study of current or former smokers. Using a k-nearest neighbor quantifier, high resolution CT chest scans were scored for percentage of normal lung, interstitial changes, and emphysema. We examined associations between QCT features and RA using multivariable linear regression. After dichotomizing participants at the 75th percentile for each QCT feature among non-RA participants, we investigated mortality associations by RA/non-RA status and quartile 4 vs quartiles 1-3 of QCT features using Cox regression. We assessed for statistical interactions between RA and QCT features. RESULTS We identified 82 RA cases and 8820 non-RA comparators. In multivariable linear regression, RA was associated with higher percentage of interstitial changes (β = 1.7 ± 0.5, p= 0.0008) but not emphysema (β = 1.3 ± 1.7, p= 0.44). Participants with RA and >75th percentile of emphysema had significantly higher mortality than non-RA participants (HR 5.86, 95%CI 3.75-9.13) as well as RA participants (HR 5.56, 95%CI 2.71-11.38) with ≤75th percentile of emphysema. There were statistical interactions between RA and emphysema for mortality (multiplicative p= 0.014; attributable proportion 0.53, 95%CI 0.30-0.70). CONCLUSIONS Using machine learning-derived QCT data in a cohort of smokers, RA was associated with higher percentage of interstitial changes. The combination of RA and emphysema conferred >5-fold higher mortality.
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Affiliation(s)
- Gregory C McDermott
- Division of Rheumatology, Inflammation, and Immunity, Brigham and Women's Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Keigo Hayashi
- Division of Rheumatology, Inflammation, and Immunity, Brigham and Women's Hospital, Boston, MA, USA
| | - Kazuki Yoshida
- Division of Rheumatology, Inflammation, and Immunity, Brigham and Women's Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Pierre-Antoine Juge
- Division of Rheumatology, Inflammation, and Immunity, Brigham and Women's Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
- Université de Paris Cité, INSERM UMR 1152, Paris, F-75018, France
- Service de Rhumatologie, Hôpital Bichat-Claude Bernard, AP-HP, Paris, F-75018, France
| | - Matthew Moll
- Harvard Medical School, Boston, MA, USA
- Division of Pulmonary and Critical Care Medicine, Brigham and Women's Hospital, Boston, MA, USA
- Channing Division of Network Medicine, Brigham and Women's Hospital, Boston, MA, USA
- Pulmonary, Allergy, Sleep and Critical Care Medicine Section, Department of Medicine, VA Boston Healthcare System, West Roxbury, USA, MA
| | - Michael H Cho
- Harvard Medical School, Boston, MA, USA
- Division of Pulmonary and Critical Care Medicine, Brigham and Women's Hospital, Boston, MA, USA
- Channing Division of Network Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Tracy J Doyle
- Harvard Medical School, Boston, MA, USA
- Division of Pulmonary and Critical Care Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Gregory L Kinney
- Colorado School of Public Health, Department of Epidemiology, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Paul F Dellaripa
- Division of Rheumatology, Inflammation, and Immunity, Brigham and Women's Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Zachary S Wallace
- Harvard Medical School, Boston, MA, USA
- Rheumatology Unit, Division of Rheumatology, Allergy, and Immunology, Massachusetts General Hospital, Boston, MA, USA
| | | | - Gary M Hunninghake
- Harvard Medical School, Boston, MA, USA
- Division of Pulmonary and Critical Care Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Edwin K Silverman
- Harvard Medical School, Boston, MA, USA
- Division of Pulmonary and Critical Care Medicine, Brigham and Women's Hospital, Boston, MA, USA
- Channing Division of Network Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Samuel Y Ash
- Harvard Medical School, Boston, MA, USA
- Division of Pulmonary and Critical Care Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Raul San Jose Estepar
- Harvard Medical School, Boston, MA, USA
- Division of Pulmonary and Critical Care Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - George R Washko
- Harvard Medical School, Boston, MA, USA
- Division of Pulmonary and Critical Care Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Jeffrey A Sparks
- Division of Rheumatology, Inflammation, and Immunity, Brigham and Women's Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
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9
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Kim JS, Montesi SB, Adegunsoye A, Humphries SM, Salisbury ML, Hariri LP, Kropski JA, Richeldi L, Wells AU, Walsh S, Jenkins RG, Rosas I, Noth I, Hunninghake GM, Martinez FJ, Podolanczuk AJ. Approach to Clinical Trials for the Prevention of Pulmonary Fibrosis. Ann Am Thorac Soc 2023; 20:1683-1693. [PMID: 37703509 PMCID: PMC10704236 DOI: 10.1513/annalsats.202303-188ps] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2023] [Accepted: 09/13/2023] [Indexed: 09/15/2023] Open
Affiliation(s)
- John S. Kim
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, University of Virginia, Charlottesville, Virginia
- Department of Medicine, Columbia University Irving Medical Center, New York, New York
| | | | - Ayodeji Adegunsoye
- Department of Medicine, The University of Chicago Medicine, Chicago, Illinois
| | | | - Margaret L. Salisbury
- Division of Allergy, Pulmonary and Critical Care Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Lida P. Hariri
- Division of Pulmonary and Critical Care Medicine, and
- Department of Pathology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts
| | - Jonathan A. Kropski
- Division of Allergy, Pulmonary and Critical Care Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Luca Richeldi
- Fondazione Policlinico Universitario Agostino Gemelli Istituto di Ricovero e Cura a Carattere Scientifico, Università Cattolica del Sacro Cuore, Rome, Italy
| | - Athol U. Wells
- Department of Radiology, and
- Interstitial Lung Disease Service, Royal Brompton Hospital, London, United Kingdom
| | - Simon Walsh
- National Heart and Lung Institute, Imperial College, London, United Kingdom
| | - R. Gisli Jenkins
- National Heart and Lung Institute, Imperial College, London, United Kingdom
| | - Ivan Rosas
- Division of Pulmonary, Critical Care, and Sleep Medicine, Department of Medicine, Baylor College of Medicine, Houston, Texas
| | - Imre Noth
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, University of Virginia, Charlottesville, Virginia
| | - Gary M. Hunninghake
- Pulmonary and Critical Care Division, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts; and
| | - Fernando J. Martinez
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, Weill Cornell Medicine, New York, New York
| | - Anna J. Podolanczuk
- Division of Pulmonary and Critical Care Medicine, Department of Medicine, Weill Cornell Medicine, New York, New York
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10
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Larici AR, Cicchetti G. The Promise of Quantitative Computed Tomographic Analysis in Assessing Progression of Interstitial Lung Abnormalities and Emphysema in Smokers. Am J Respir Crit Care Med 2023; 208:645-647. [PMID: 37478327 PMCID: PMC10515567 DOI: 10.1164/rccm.202306-1063ed] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2023] [Accepted: 07/19/2023] [Indexed: 07/23/2023] Open
Affiliation(s)
- Anna Rita Larici
- Dipartimento di Scienze Radiologiche ed Ematologiche Universita' Cattolica del Sacro Cuore di Roma, Italia
- Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia Fondazione Policlinico Universitario "A. Gemelli" IRCCS Roma, Italia
| | - Giuseppe Cicchetti
- Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia Fondazione Policlinico Universitario "A. Gemelli" IRCCS Roma, Italia
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